Overview

Dataset statistics

Number of variables14
Number of observations506
Missing cells120
Missing cells (%)1.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory55.5 KiB
Average record size in memory112.3 B

Variable types

NUM13
BOOL1

Warnings

TAX is highly correlated with RADHigh correlation
RAD is highly correlated with TAXHigh correlation
CRIM has 20 (4.0%) missing values Missing
ZN has 20 (4.0%) missing values Missing
INDUS has 20 (4.0%) missing values Missing
CHAS has 20 (4.0%) missing values Missing
AGE has 20 (4.0%) missing values Missing
LSTAT has 20 (4.0%) missing values Missing
ZN has 360 (71.1%) zeros Zeros

Reproduction

Analysis started2023-01-08 06:21:46.810638
Analysis finished2023-01-08 06:22:58.801596
Duration1 minute and 11.99 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

CRIM
Real number (ℝ≥0)

MISSING

Distinct484
Distinct (%)99.6%
Missing20
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean3.611873971
Minimum0.00632
Maximum88.9762
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2023-01-08T11:52:59.165693image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.00632
5-th percentile0.02739
Q10.0819
median0.253715
Q33.5602625
95-th percentile15.870875
Maximum88.9762
Range88.96988
Interquartile range (IQR)3.4783625

Descriptive statistics

Standard deviation8.72019185
Coefficient of variation (CV)2.414312326
Kurtosis36.56834838
Mean3.611873971
Median Absolute Deviation (MAD)0.218875
Skewness5.21284265
Sum1755.37075
Variance76.0417459
MonotocityNot monotonic
2023-01-08T11:52:59.457763image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.0150120.4%
 
14.333720.4%
 
0.0454410.2%
 
0.0249810.2%
 
0.0130110.2%
 
0.0615110.2%
 
0.0549710.2%
 
0.0330610.2%
 
0.0304110.2%
 
0.0342710.2%
 
Other values (474)47493.7%
 
(Missing)204.0%
 
ValueCountFrequency (%) 
0.0063210.2%
 
0.0090610.2%
 
0.0109610.2%
 
0.0130110.2%
 
0.0131110.2%
 
ValueCountFrequency (%) 
88.976210.2%
 
73.534110.2%
 
67.920810.2%
 
51.135810.2%
 
45.746110.2%
 

ZN
Real number (ℝ≥0)

MISSING
ZEROS

Distinct26
Distinct (%)5.3%
Missing20
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean11.21193416
Minimum0
Maximum100
Zeros360
Zeros (%)71.1%
Memory size4.0 KiB
2023-01-08T11:52:59.734833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312.5
95-th percentile80
Maximum100
Range100
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation23.38887615
Coefficient of variation (CV)2.086069702
Kurtosis4.132614189
Mean11.21193416
Median Absolute Deviation (MAD)0
Skewness2.256612605
Sum5449
Variance547.0395274
MonotocityNot monotonic
2023-01-08T11:52:59.983898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%) 
036071.1%
 
20204.0%
 
80142.8%
 
22102.0%
 
25102.0%
 
12.5102.0%
 
4061.2%
 
4561.2%
 
9051.0%
 
3051.0%
 
Other values (16)407.9%
 
(Missing)204.0%
 
ValueCountFrequency (%) 
036071.1%
 
12.5102.0%
 
17.510.2%
 
1810.2%
 
20204.0%
 
ValueCountFrequency (%) 
10010.2%
 
9540.8%
 
9051.0%
 
8520.4%
 
82.520.4%
 

INDUS
Real number (ℝ≥0)

MISSING

Distinct76
Distinct (%)15.6%
Missing20
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean11.08399177
Minimum0.46
Maximum27.74
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2023-01-08T11:53:00.288971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.46
5-th percentile2.18
Q15.19
median9.69
Q318.1
95-th percentile21.3125
Maximum27.74
Range27.28
Interquartile range (IQR)12.91

Descriptive statistics

Standard deviation6.835896499
Coefficient of variation (CV)0.6167359775
Kurtosis-1.217990915
Mean11.08399177
Median Absolute Deviation (MAD)6.32
Skewness0.3037221876
Sum5386.82
Variance46.72948094
MonotocityNot monotonic
2023-01-08T11:53:00.604053image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
18.112725.1%
 
19.58285.5%
 
8.14224.3%
 
6.2183.6%
 
21.89142.8%
 
9.9122.4%
 
3.97122.4%
 
8.56112.2%
 
10.59112.2%
 
5.8691.8%
 
Other values (66)22243.9%
 
(Missing)204.0%
 
ValueCountFrequency (%) 
0.4610.2%
 
0.7410.2%
 
1.2110.2%
 
1.2210.2%
 
1.2520.4%
 
ValueCountFrequency (%) 
27.7451.0%
 
25.6561.2%
 
21.89142.8%
 
19.58285.5%
 
18.112725.1%
 

CHAS
Boolean

MISSING

Distinct2
Distinct (%)0.4%
Missing20
Missing (%)4.0%
Memory size4.0 KiB
0
452 
1
 
34
(Missing)
 
20
ValueCountFrequency (%) 
045289.3%
 
1346.7%
 
(Missing)204.0%
 
2023-01-08T11:53:00.897125image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

NOX
Real number (ℝ≥0)

Distinct81
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5546950593
Minimum0.385
Maximum0.871
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2023-01-08T11:53:01.204206image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.385
5-th percentile0.40925
Q10.449
median0.538
Q30.624
95-th percentile0.74
Maximum0.871
Range0.486
Interquartile range (IQR)0.175

Descriptive statistics

Standard deviation0.1158776757
Coefficient of variation (CV)0.2089033853
Kurtosis-0.06466713337
Mean0.5546950593
Median Absolute Deviation (MAD)0.0875
Skewness0.7293079225
Sum280.6757
Variance0.01342763572
MonotocityNot monotonic
2023-01-08T11:53:01.724335image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.538234.5%
 
0.713183.6%
 
0.437173.4%
 
0.871163.2%
 
0.624153.0%
 
0.489153.0%
 
0.693142.8%
 
0.605142.8%
 
0.74132.6%
 
0.544122.4%
 
Other values (71)34969.0%
 
ValueCountFrequency (%) 
0.38510.2%
 
0.38910.2%
 
0.39220.4%
 
0.39410.2%
 
0.39820.4%
 
ValueCountFrequency (%) 
0.871163.2%
 
0.7781.6%
 
0.74132.6%
 
0.71861.2%
 
0.713183.6%
 

RM
Real number (ℝ≥0)

Distinct446
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.284634387
Minimum3.561
Maximum8.78
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2023-01-08T11:53:02.174448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3.561
5-th percentile5.314
Q15.8855
median6.2085
Q36.6235
95-th percentile7.5875
Maximum8.78
Range5.219
Interquartile range (IQR)0.738

Descriptive statistics

Standard deviation0.7026171434
Coefficient of variation (CV)0.1117992074
Kurtosis1.891500366
Mean6.284634387
Median Absolute Deviation (MAD)0.3455
Skewness0.4036121333
Sum3180.025
Variance0.4936708502
MonotocityNot monotonic
2023-01-08T11:53:02.670574image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5.71330.6%
 
6.16730.6%
 
6.12730.6%
 
6.22930.6%
 
6.40530.6%
 
6.41730.6%
 
6.78220.4%
 
6.95120.4%
 
6.6320.4%
 
6.31220.4%
 
Other values (436)48094.9%
 
ValueCountFrequency (%) 
3.56110.2%
 
3.86310.2%
 
4.13820.4%
 
4.36810.2%
 
4.51910.2%
 
ValueCountFrequency (%) 
8.7810.2%
 
8.72510.2%
 
8.70410.2%
 
8.39810.2%
 
8.37510.2%
 

AGE
Real number (ℝ≥0)

MISSING

Distinct348
Distinct (%)71.6%
Missing20
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean68.51851852
Minimum2.9
Maximum100
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2023-01-08T11:53:03.233717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile17.95
Q145.175
median76.8
Q393.975
95-th percentile100
Maximum100
Range97.1
Interquartile range (IQR)48.8

Descriptive statistics

Standard deviation27.99951301
Coefficient of variation (CV)0.4086415412
Kurtosis-0.9821403245
Mean68.51851852
Median Absolute Deviation (MAD)20.15
Skewness-0.5824700575
Sum33300
Variance783.9727285
MonotocityNot monotonic
2023-01-08T11:53:03.750848image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
100428.3%
 
97.940.8%
 
87.940.8%
 
98.840.8%
 
9640.8%
 
95.440.8%
 
76.530.6%
 
9730.6%
 
96.230.6%
 
32.230.6%
 
Other values (338)41281.4%
 
(Missing)204.0%
 
ValueCountFrequency (%) 
2.910.2%
 
6.210.2%
 
6.510.2%
 
6.620.4%
 
6.810.2%
 
ValueCountFrequency (%) 
100428.3%
 
99.310.2%
 
99.110.2%
 
98.930.6%
 
98.840.8%
 

DIS
Real number (ℝ≥0)

Distinct412
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.795042688
Minimum1.1296
Maximum12.1265
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2023-01-08T11:53:04.320993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.1296
5-th percentile1.461975
Q12.100175
median3.20745
Q35.188425
95-th percentile7.8278
Maximum12.1265
Range10.9969
Interquartile range (IQR)3.08825

Descriptive statistics

Standard deviation2.105710127
Coefficient of variation (CV)0.5548580872
Kurtosis0.4879411222
Mean3.795042688
Median Absolute Deviation (MAD)1.29115
Skewness1.011780579
Sum1920.2916
Variance4.434015137
MonotocityNot monotonic
2023-01-08T11:53:04.892136image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3.495251.0%
 
5.720940.8%
 
5.287340.8%
 
6.814740.8%
 
5.400740.8%
 
6.336130.6%
 
3.945430.6%
 
6.49830.6%
 
4.721130.6%
 
4.812230.6%
 
Other values (402)47092.9%
 
ValueCountFrequency (%) 
1.129610.2%
 
1.13710.2%
 
1.169110.2%
 
1.174210.2%
 
1.178110.2%
 
ValueCountFrequency (%) 
12.126510.2%
 
10.710320.4%
 
10.585720.4%
 
9.222910.2%
 
9.220320.4%
 

RAD
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.549407115
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2023-01-08T11:53:05.329248image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q324
95-th percentile24
Maximum24
Range23
Interquartile range (IQR)20

Descriptive statistics

Standard deviation8.707259384
Coefficient of variation (CV)0.9118115166
Kurtosis-0.8672319936
Mean9.549407115
Median Absolute Deviation (MAD)2
Skewness1.004814648
Sum4832
Variance75.81636598
MonotocityNot monotonic
2023-01-08T11:53:05.713343image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
2413226.1%
 
511522.7%
 
411021.7%
 
3387.5%
 
6265.1%
 
2244.7%
 
8244.7%
 
1204.0%
 
7173.4%
 
ValueCountFrequency (%) 
1204.0%
 
2244.7%
 
3387.5%
 
411021.7%
 
511522.7%
 
ValueCountFrequency (%) 
2413226.1%
 
8244.7%
 
7173.4%
 
6265.1%
 
511522.7%
 

TAX
Real number (ℝ≥0)

HIGH CORRELATION

Distinct66
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean408.2371542
Minimum187
Maximum711
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2023-01-08T11:53:06.178461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum187
5-th percentile222
Q1279
median330
Q3666
95-th percentile666
Maximum711
Range524
Interquartile range (IQR)387

Descriptive statistics

Standard deviation168.5371161
Coefficient of variation (CV)0.4128411987
Kurtosis-1.142407992
Mean408.2371542
Median Absolute Deviation (MAD)73
Skewness0.6699559418
Sum206568
Variance28404.75949
MonotocityNot monotonic
2023-01-08T11:53:06.704594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
66613226.1%
 
307407.9%
 
403305.9%
 
437153.0%
 
304142.8%
 
264122.4%
 
398122.4%
 
384112.2%
 
277112.2%
 
224102.0%
 
Other values (56)21943.3%
 
ValueCountFrequency (%) 
18710.2%
 
18871.4%
 
19381.6%
 
19810.2%
 
21651.0%
 
ValueCountFrequency (%) 
71151.0%
 
66613226.1%
 
46910.2%
 
437153.0%
 
43291.8%
 

PTRATIO
Real number (ℝ≥0)

Distinct46
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.4555336
Minimum12.6
Maximum22
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2023-01-08T11:53:07.258734image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum12.6
5-th percentile14.7
Q117.4
median19.05
Q320.2
95-th percentile21
Maximum22
Range9.4
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.164945524
Coefficient of variation (CV)0.1173060379
Kurtosis-0.2850913833
Mean18.4555336
Median Absolute Deviation (MAD)1.15
Skewness-0.8023249269
Sum9338.5
Variance4.686989121
MonotocityNot monotonic
2023-01-08T11:53:07.790871image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%) 
20.214027.7%
 
14.7346.7%
 
21275.3%
 
17.8234.5%
 
19.2193.8%
 
17.4183.6%
 
18.6173.4%
 
19.1173.4%
 
18.4163.2%
 
16.6163.2%
 
Other values (36)17935.4%
 
ValueCountFrequency (%) 
12.630.6%
 
13122.4%
 
13.610.2%
 
14.410.2%
 
14.7346.7%
 
ValueCountFrequency (%) 
2220.4%
 
21.2153.0%
 
21.110.2%
 
21275.3%
 
20.9112.2%
 

B
Real number (ℝ≥0)

Distinct357
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean356.6740316
Minimum0.32
Maximum396.9
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2023-01-08T11:53:08.408025image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.32
5-th percentile84.59
Q1375.3775
median391.44
Q3396.225
95-th percentile396.9
Maximum396.9
Range396.58
Interquartile range (IQR)20.8475

Descriptive statistics

Standard deviation91.29486438
Coefficient of variation (CV)0.255961624
Kurtosis7.226817549
Mean356.6740316
Median Absolute Deviation (MAD)5.46
Skewness-2.890373712
Sum180477.06
Variance8334.752263
MonotocityNot monotonic
2023-01-08T11:53:08.955162image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
396.912123.9%
 
393.7430.6%
 
395.2430.6%
 
376.1420.4%
 
394.7220.4%
 
395.6320.4%
 
392.820.4%
 
395.5620.4%
 
390.9420.4%
 
393.6820.4%
 
Other values (347)36572.1%
 
ValueCountFrequency (%) 
0.3210.2%
 
2.5210.2%
 
2.610.2%
 
3.510.2%
 
3.6510.2%
 
ValueCountFrequency (%) 
396.912123.9%
 
396.4210.2%
 
396.3310.2%
 
396.310.2%
 
396.2810.2%
 

LSTAT
Real number (ℝ≥0)

MISSING

Distinct438
Distinct (%)90.1%
Missing20
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean12.7154321
Minimum1.73
Maximum37.97
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2023-01-08T11:53:09.556313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.73
5-th percentile3.7075
Q17.125
median11.43
Q316.955
95-th percentile27.15
Maximum37.97
Range36.24
Interquartile range (IQR)9.83

Descriptive statistics

Standard deviation7.155870816
Coefficient of variation (CV)0.5627705579
Kurtosis0.5186825176
Mean12.7154321
Median Absolute Deviation (MAD)4.795
Skewness0.908891837
Sum6179.7
Variance51.20648713
MonotocityNot monotonic
2023-01-08T11:53:10.091452image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
7.7930.6%
 
6.3630.6%
 
8.0530.6%
 
14.130.6%
 
18.1330.6%
 
30.8120.4%
 
4.5920.4%
 
7.3920.4%
 
12.6720.4%
 
5.2920.4%
 
Other values (428)46191.1%
 
(Missing)204.0%
 
ValueCountFrequency (%) 
1.7310.2%
 
1.9210.2%
 
1.9810.2%
 
2.4710.2%
 
2.8710.2%
 
ValueCountFrequency (%) 
37.9710.2%
 
36.9810.2%
 
34.7710.2%
 
34.4110.2%
 
34.3710.2%
 

Price
Real number (ℝ≥0)

Distinct229
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.53280632
Minimum5
Maximum50
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2023-01-08T11:53:10.497550image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10.2
Q117.025
median21.2
Q325
95-th percentile43.4
Maximum50
Range45
Interquartile range (IQR)7.975

Descriptive statistics

Standard deviation9.197104087
Coefficient of variation (CV)0.408165053
Kurtosis1.495196944
Mean22.53280632
Median Absolute Deviation (MAD)4
Skewness1.108098408
Sum11401.6
Variance84.58672359
MonotocityNot monotonic
2023-01-08T11:53:10.826639image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
50163.2%
 
2581.6%
 
2271.4%
 
21.771.4%
 
23.171.4%
 
19.461.2%
 
20.661.2%
 
13.851.0%
 
21.451.0%
 
20.151.0%
 
Other values (219)43485.8%
 
ValueCountFrequency (%) 
520.4%
 
5.610.2%
 
6.310.2%
 
720.4%
 
7.230.6%
 
ValueCountFrequency (%) 
50163.2%
 
48.810.2%
 
48.510.2%
 
48.310.2%
 
46.710.2%
 

Interactions

2023-01-08T11:52:09.256305image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:09.613394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:09.927475image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:10.274563image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:10.601643image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:10.957736image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:11.307825image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:11.628902image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:11.962990image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:12.255060image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:12.496124image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:12.797197image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:13.061266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:13.264317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:13.468366image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:13.680419image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:13.903476image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:14.119533image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:14.326584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:14.671673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:15.023782image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:15.389852image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:15.735943image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:16.084029image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:16.471128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:16.866226image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:17.235320image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:17.845476image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:18.208568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:18.561655image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:18.894742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:19.263832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:19.646931image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:20.008023image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:20.354110image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:20.714199image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:21.049284image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:21.430381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:21.821481image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:22.086543image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:22.294597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:22.501652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:22.711703image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:22.911752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:23.116805image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:23.321856image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:23.529909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:23.735963image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:23.944016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:24.150068image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:24.369123image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:24.588178image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:24.792229image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:24.994279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:25.206334image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:25.413384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:25.621440image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:25.825489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:26.036545image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:26.245596image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:26.459649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:26.670705image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:26.876755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:27.372882image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:27.630946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:27.873009image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:28.096065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:28.300117image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:28.508168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:28.717221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:28.924274image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:29.134327image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:29.339377image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:29.548432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:29.760485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:29.966538image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:30.200598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:30.463662image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:30.827756image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:31.184847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:31.526932image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:31.855016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:32.230109image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:32.601202image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:33.087327image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:33.442416image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:33.775498image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:34.105584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:34.458671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:34.824763image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:35.176852image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:35.481930image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:35.839023image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:36.162101image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:36.467181image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:36.813270image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:37.160356image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:37.514448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:37.852529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:38.082589image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:38.296642image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:38.500691image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:38.723750image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:38.943805image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:39.154859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:39.360912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:39.573964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:40.127104image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:40.350161image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:40.558212image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:40.767266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:40.974318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:41.186370image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:41.396422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:41.605479image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:41.829532image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:42.057596image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:42.289651image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:42.487698image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:42.700752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:42.907806image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:43.107858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:43.309909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:43.513959image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:43.715009image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:43.927064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:44.133114image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:44.331170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:44.552224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:44.769275image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:44.969329image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:45.197386image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:45.428446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:45.657501image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:45.887562image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:46.117618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:46.348675image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:46.745776image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:47.194943image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:47.590046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:47.953135image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:48.386244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:48.805349image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:49.209451image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:49.608552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:49.995651image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:50.351739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:50.665819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:51.098931image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:51.488027image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:51.875127image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:52.232215image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:52.570300image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:52.931394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:53.328494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:53.683585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:53.990661image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:54.223720image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:54.428771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:54.633823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:55.259979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:55.491039image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:55.711095image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:55.915147image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:56.117197image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:56.324248image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:56.523300image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:56.740355image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:56.952409image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-01-08T11:53:11.102707image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-01-08T11:53:11.509807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-01-08T11:53:12.507061image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-01-08T11:53:12.944170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-01-08T11:52:57.390421image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:57.941561image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:58.311060image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-08T11:52:58.519526image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Sample

First rows

CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATPrice
00.0118.002.310.000.546.5865.204.09129615.30396.904.9824.00
10.030.007.070.000.476.4278.904.97224217.80396.909.1421.60
20.030.007.070.000.477.1861.104.97224217.80392.834.0334.70
30.030.002.180.000.467.0045.806.06322218.70394.632.9433.40
40.070.002.180.000.467.1554.206.06322218.70396.90NaN36.20
50.030.002.180.000.466.4358.706.06322218.70394.125.2128.70
60.0912.507.87NaN0.526.0166.605.56531115.20395.6012.4322.90
70.1412.507.870.000.526.1796.105.95531115.20396.9019.1527.10
80.2112.507.870.000.525.63100.006.08531115.20386.6329.9316.50
90.1712.507.87NaN0.526.0085.906.59531115.20386.7117.1018.90

Last rows

CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATPrice
4960.290.009.690.000.585.3972.902.80639119.20396.9021.1419.70
4970.270.009.690.000.585.7970.602.89639119.20396.9014.1018.30
4980.240.009.690.000.586.0265.302.41639119.20396.9012.9221.20
4990.180.009.690.000.585.5773.502.40639119.20395.7715.1017.50
5000.220.009.690.000.586.0379.702.50639119.20396.9014.3316.80
5010.060.0011.930.000.576.5969.102.48127321.00391.99NaN22.40
5020.050.0011.930.000.576.1276.702.29127321.00396.909.0820.60
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